On-shelf image based out-of-stock detection

ABSTRACT

An out-of-stock detection system notifies store management that a product is out of stock. The system captures images of a shelf and determines the position product labels thereon. For each product label, a bounding box is generated based on the position of each product label on the shelf. The system then identifies a product for each product label based on information within each product label and, for each product label, stores a product identified for each bounding box. Accordingly, the system performs an out-of-stock detection process that includes capturing additional image data of the shelf periodically that includes each bounding box, providing a portion of the additional image data for each bounding box to a model trained to determine whether the bounding box contains products, sending a notification for a product determined to be out of stock to a store client device based on output from the model.

BACKGROUND

Stores offer items for sale to shoppers who visit the stores. Asshoppers purchase the items that are available for sale, storemanagement must continually restock items as shoppers purchase them.However, different items may need to be restocked at different rates andthe rates at which items need to be restocked may change over time, andthus it is advantageous for store management to have up-to-dateinformation on what items need to be restocked. Typically, in order toreceive the up-to-date information, an employee of the store regularlytraverses the store to identify which items need to be restocked. Thisrequires a significant amount of employee-hours during the operation ofthe store. While store management can reduce the regularity with whichan employee traverses the store, this comes at the cost of itemsremaining out of stock for greater periods of time during which shopperscannot purchase them. Store management must balance employee time withrestocking items, which itself requires resources from store management.As such, conventional methods for identifying out of stock items requiresignificant resources from stores, thus increasing the stores' expenses.

SUMMARY

An out-of-stock detection system notifies store management that aproduct is out of stock. In an onboarding setup stage, the out-of-stockdetection system obtains one or more images of a product display area ina store captured by a camera mounted on the product display area. Theimage of the product display area is analyzed to detect product labels,such as price tags or product identifiers attached to the productdisplay area identifying a location for a product described by theproduct label. Retail store convention may position a product on a shelfabove and to the right of its corresponding product label (or viceversa, depending on the store). Thus, using the location of a productlabel on a shelf of the product display area, the out-of-stock detectionsystem defines a bounding box for a portion of the shelf that shouldcontain the product. The out-of-stock detection system defines acoordinate system (e.g., x, y coordinates of the product display area oralpha, beta angles of the camera) that identifies the position of thebounding box within or with respect to the product display area orcamera and stores the position of each bounding box for out-of-stockdetection.

In a deployment stage after the onboarding setup stage, images of theproduct display area are periodically captured by the camera. Theportion of the shelf corresponding to each bounding box determined andstored in the onboarding stage is extracted from the images and providedto a trained classifier model that outputs a prediction of whether thisportion of the shelf is less than full and, therefore, requirerestocking or the store associates attention. Accordingly, theclassifier outputs an out-of-stock prediction for each product and theout-of-stock detection system generates a notification to a storeassociate for products whose corresponding portion of the shelf is atleast less than full based on the classifier's prediction.

An out-of-stock detection system as described herein allows a store'smanagement to have up-to-date information on which items are out ofstock within the store, so items can be restocked more quickly. It alsoremoves the need for a store employee to travel through the store todetermine which items are out of stock, thereby reducing the number ofemployee-hours that the store spends on restocking items. Furthermore,the out-of-stock detection system can collect and analyze data on therate at which items need to be restocked and can present the analyzeddata to the store management for more information on how often itemsneed to be restocked.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example camera mounted on a shelf in a store tocapture images of products for out-of-stock detection, in accordancewith various embodiments.

FIG. 2 illustrates an example image received from a camera for traininga product-detection model, in accordance with various embodiments.

FIG. 3 illustrates an example image received from a camera forperforming out-of-stock detection by the product-detection model, inaccordance with various embodiments.

FIG. 4 is a flowchart for a method of detecting out-of-stock items basedon images captured by one or more cameras mounted on shelves in a store,in accordance with various embodiments.

FIG. 5 illustrates an example enlarged view of a portion of the imagereceived from the camera in FIG. 2, in accordance with variousembodiments.

FIG. 6 shows an example top view of multiple cameras and theircorresponding fields of view mounted on top of shelves in a store, inaccordance with various embodiments.

FIG. 7 shows an example top view of shelves in a store that each includea track along which a camera is movable mounted, in accordance withvarious embodiments.

FIG. 8 shows an example side view of a shelf in a store that include acable hanging above along which a camera is movable mounted, inaccordance with various embodiments.

FIG. 9 illustrates an example system environment and architecture for anout-of-stock detection system, in accordance with some embodiments.

DETAILED DESCRIPTION Shelf Camera for Out-of-Stock Detection Overview

FIG. 1 illustrates an example camera 100 mounted on a shelf unit 105 ain a store to capture images of products for out-of-stock detection.Camera 100 is one of many such cameras that may be deployed on shelfunits within the store. For example, for an aisle of length L, thenumber of cameras 100 deployed to cover the length of the aisle isdependent on the field of view of the cameras 100 and the desired degreeof overlap between them for applications, such as image stitching. Asdiscussed below, the cameras 100 may additionally zoom-in, rotate,and/or move along a track or cable, in other embodiments, to minimizethe number of cameras 100 deployed within a store and for various otherapplications. For fixed location cameras, however, each camera 100 isassociated with a unique camera ID that is mapped to the camera'slocation within the store. As mentioned above, stores are organized in apredetermined layout or planogram that identifies where a particularproduct or category of products is located within the store. Thus, thecamera's location indicated by its unique camera ID is associated with apredetermined set of products within the camera's field of view based onthe planogram of the store. Each camera 100, in this instance, collectsimage data for a predetermined set of products displayed on the opposingshelf units relative to the shelf unit on which a particular camera ismounted for on-shelf inventory tracking and out of stock detection.Similarly, for movable cameras, a combination of camera ID and cameraposition along the track or cable can be used to identify which productsshould be within the camera's field of view.

In order for the out-of-stock detection system to register the camera100 to its location within the store and associate it with thepredetermined set of products within its field of view, an initialonboarding process is performed to train a product-detection model. Inone embodiment, each camera 100 captures image data within the field ofview of the camera 100 and sends the image data to the out-of-stockdetection system where it is processed to identify the products withinthe cameras field of view.

FIG. 2 illustrates an example image of shelf unit 105 b standingopposite shelf unit 105 a, upon which camera 100 in FIG. 1 is mounted.Shelf unit 105 b includes multiple different products 110 and a productlabel 115 associated with each product 110 that may include a price tag.The out-of-stock detection system identifies product labels 115 withinthe image and a bounding box 120 for each product label 115 is generatedbased on the location of the product label 115. In one embodiment, thebounding boxes 120 are generated using a deep learning computer visionalgorithm that identifies a product label 115 and generates a horizontalbound of the bounding box 120 at the product label 115 to the right (orleft, depending on convention) of the product label 115 until anotheradjacent product label 115 is identified. This process can be repeatedfor the vertical bound of the bounding box 120 where the height of thebounding box 120 is determined as the vertical space between productlabels 115. This process works for products on the bottom and middleshelves and other heuristics can be used to identify whether aparticular bounding box 120 is on the top shelf. This may includeidentifying a product label 115 for a top shelf product based on thecamera angle associated with the product label 115, which could beidentified using a Deep Learning model or a labeler or identifiedmanually, in some instances, by a human, such as a store associate.

Once the bounding boxes 120 are generated for each product label 115,the product 110 associated with each bounding box 120 is identified. Asused herein, a product 110 associated with a bounding box 120 is an itemof the same type and of the same brand. For example, a shelf unit maydisplay multiple different brands and kinds of soap. Thus, as usedherein, a product 110 refers to the same kind of soap being offeredunder the same brand name and each of the different brands and kinds ofsoap would have their own bounding box 120. Moreover, a product label115 identifies a product display location for displaying the same typeof product of the same brand, which is bounded by a bounding box 120.

Accordingly, there are multiple ways to identify the product 110associated with each bounding box 120. In one embodiment, theout-of-stock detection system extracts product information from eachproduct label 115, which includes information describing the product 110associated with the product label 115. This information includes theproduct name and brand, price, barcode, and stock keeping unit (SKU)number, among potentially other types of information, and theout-of-stock detection system extracts this information to identify theproduct 110 associated with each product label 115 and, therefore,bounding box 120.

In one embodiment, an optical character recognition (OCR) algorithm isused to identify text of the product labels 115. Additionally, a barcodedetection algorithm could be used to detect barcodes within the productlabel 115 and identify the products 110 based on the barcodes. Whileeach camera 100 is capable of operating at a low image resolution toconserve energy, the cameras 100 may capture relatively high-resolutionimages during the onboarding process, and may optionally zoom in on eachproduct label 115 individually in order to be able to clearly identifythis information within each product label 115.

Similarly, instead of extracting information from the product labels115, the out-of-stock detection system may extract information from theactual products 110 themselves or use this information to supplementthat of the information extracted from the product labels 115. Moreover,the out-of-stock detection system may supplement the product informationextraction process using the planogram of the store. For example, theout-of-stock detection system may obtain information for a particularshelf unit 105 from the planogram of the store that identifies whatproducts should be on that particular shelf unit based on the camera IDfor a particular camera 100 associated with the template image orimages. The out-of-stock detection system may then use this informationto narrow down or filter the extracted product information from eachproduct label 115 to identify the product 110 associated with theproduct label 115. Alternatively, the out-of-stock detection system mayidentify a portion of the planogram corresponding to a particular shelfunit 105 and identify the products 110 on the shelf unit 105 byoverlaying the planogram onto the image shelf unit 105 by, for example,spatially matching bounding boxes.

In another embodiment, the out-of-stock detection system presents theimage with the generated bounding box 120 for each product label 115 fordisplay to a store associate who manually identifies the product 110associated with each bounding box 120 and inputs identifying informationfor the product 110, such as the SKU number, into an input field foreach bounding box 120 provided in a user interface by the out-of-stockdetection system.

The out-of-stock detection system associates the identified product 110with its corresponding bounding box 110 for each product label 115.Accordingly, the out-of-stock detection system stores this informationfor later retrieval during a deployment stage of the process when thecameras 100 periodically scan their corresponding shelf unit.

Moreover, the camera can be equipped with a small motor that will panthe camera from the top left of each shelf unit 105, to the top right ofthe shelf unit 105, angle downward a bit, then repeat the process again,and again until the camera covers the entire field of view to the bottomright of the shelf unit 105. Effectively scanning the entire viewablearea of the camera (or the entire shelf unit 105) while only requiring a1-5 MP camera while zoomed-in. There will likely be overlapping pixelsfrom adjacent images to allow for, in one embodiment, image stitchingand the camera 100 may transmit each of the captured images along withtheir corresponding alpha and beta angles of the camera (from 0,0).Accordingly, once these images are received by the system, the systemmay optionally stitch the images into a single large image. Then, ahuman store associate can select at least a subset of products to trackand receive notifications for restocking out-of-stock items. Thus, inone embodiment, the store associate selection may cause the systemselect particular alpha and beta angles for the camera corresponding tothe position of selected bounding boxes for selected products and thesystem will only collect image data at these camera positions ongoing totrack the store associates chosen products. The store associate can alsoset a capture frequency of the camera 100 to run product detection at aset period of time (e.g., every half hour, hour, etc.). The morefrequent the capture frequency, the more energy is used which translatesto greater operational and system costs. The lower the capturefrequency, the lower the operational and potentially system costs,however, this comes with less “instantaneous” reporting. Thus, theout-of-stock detection system maintains template images or templateimage data that cover at least a subset of the shelf units and otherdisplay areas (e.g., shelf units, display tables, refrigerators, displayareas of a produce section, etc.) of the store.

Accordingly, in the deployment stage, the out-of-stock detection systemreceives image data of one or more shelf units captured by a camera 100periodically to perform product detection. In one embodiment, the imagedata received in the deployment stage is captured only for the alpha andbeta angles of the camera for each bounding box 120 (or a subset thereofselected by the store associate). This may include a zoomed-in scan ofeach bounding box 120 for the identified angles. Alternatively, theimage data captured in the deployment stage may include the entire fieldof view of the camera 100.

FIG. 3 illustrates an example image of shelf unit 105 b received fromcamera 100 for performing out-of-stock detection by theproduct-detection model, in accordance with various embodiments. WhileFIG. 2 is described as an onboarding image of shelf unit 105 b, FIG. 3shows another image of shelf unit 105 b captured during out-of-stockdetection in the deployment stage, after the onboarding stage, and aftersome of the products 110 have been removed (i.e., purchased) and,therefore, out-of-stock. Thus, in this embodiment, the product-detectionmodel receives image data for the bounding boxes 120 identified in theonboarding stage and performs image classification of the images todetermine whether the product display area is full or not. In oneembodiment, the product-detection model is a classifier configured toidentify one or more voids within a bounding box 120. Thus, in thisexample, in response to determining that the bounding box 120 for “soda”is less than full (i.e., empty in, this example), the out-of-stockdetection system generates an out-of-stock notification for “soda” to astore client device. Thus, out-of-stock can be defined in a number ofways according to the desire of a store. For example, a product can beconsidered out-of-stock when a single item is missing, when the shelf isempty, or somewhere in-between. The definition or classification ofout-of-stock can be different for different products. For large items,out-of-stock may be a single item. For small items, such as cannedgoods, out-of-stock may be defined as a number of voids. Moreover, theturn or rate at which an item sells can also determine how a storeclassifies whether something is out-of-stock or not. For example, astore could define a fast moving or popular item as out-of-stock when asingle item is removed from a shelf and vice versa.

Example Methods for Detecting Out-of-Stock Items

FIG. 4 is a flowchart for an example method for an out-of-stockdetection system that includes both an onboarding stage and a deploymentstage, in accordance with some embodiments. Alternate embodiments mayinclude more, fewer, or different steps from those illustrated in FIG.4, and the steps may be performed in a different order from thatillustrated in FIG. 4. Additionally, each of these steps may beperformed automatically by the out-of-stock detection system withouthuman intervention.

In the onboarding stage, the out-of-stock detection system obtains 405first image data of a product display area in a store captured by one ormore cameras 100. The product display area includes a number of productlabels 115 each identifying a product 110 and the system determines theposition of each product label 115 within the product display area.Accordingly, based on the position of the product labels 115, the systemgenerates 410 a bounding box 120. The system then identifies 415 theproduct 110 associated with each bounding box 120. As described above,this may include extracting product information from each product labelusing an OCR algorithm, for example, to identify text of the productlabels. This may also include using a barcode detection algorithm todetect barcodes within the product label and identify the products fromthe barcodes. Similarly, instead of extracting information from theproduct labels, the system may extract information from the actualproducts. With the product 110 identified for each bounding box 120, thesystem stores 420 the identified product 110 with the correspondingbounding box 120.

In the deployment stage, the out-of-stock detection system receives 425second image data of the product display area for each bounding box 120.As described above, the second image data may be solely limited tocoordinates (i.e., alpha and beta angles of camera 100) for eachbounding box 120 selected from tracking by a store associate or thesecond image data can include an image of the entire shelf unit ordisplay area. In one embodiment, the system provides 430 the area ofeach bounding box 120 identified by the coordinates of the bounding box120 to a classifier trained to determine presence of products 110 withineach bounding box 120. In this example, the system classifier of thesystem identifies 435 one or more voids within a bounding box 120.Accordingly, in response to identifying 435 a void in the bounding box120, the system identifies 440 the product 110 associated with thebounding box 120 as being out-of-stock and sends 445 a restockingnotification for identified product 110 to a store client device.

In another embodiment, the out-of-stock detection system collects imagedata from one or more cameras in a store mounted to, for example, thestore shelves. The out-of-stock detection system detects products,voids, and product labels in the image data and determines whether anyproducts are out of stock based on the information detected in theimage. For example, the out-of-stock detection system may detect a voidon the shelf, and will then look at the product label underneath thevoid to see what should have been there, and determine that that productis out of stock. In another embodiment, the out-of-stock detectionsystem does not identify the product in the deployment state, but ratherdetermines whether a given bounding box is full or not (i.e., there areone or more voids in the bounding box or the bounding box is full) wherethe position (alpha and beta angles) of that bounding box are stored inassociation with a product that was identified in the onboarding stage.Thus, in the deployment stage, the system identifies a bounding box 120as being less than full, sends an out-of-stock notification to thesystem for that particular bounding box 120, and the system matches thebounding box to a product, which it has stored in a look-up table, forexample. Thus, upon identifying a product as out-of-stock, theout-of-stock detection system notifies the store management that theitem is out of stock.

In another embodiment, one or more cameras of the out-of-stock detectionsystem capture a template image of a fully stocked product display areafor each product display area in a store. The system identifies productlabels in the template image and generates a bounding box for eachproduct label based on a location of each product label within theproduct display area. From each product label, the system identifiesproduct information from each product label.

Accordingly, using the identified product information, the systemidentifies a product associated with each product label. In oneembodiment, the system extracts product information from each productlabel, which includes information describing the product associated withthe product label, as described above. With the product identified foreach bounding box, the system associates the identified product with thecorresponding bounding box. The system then generates individual productbounding boxes for each individual product within a single bounding boxfor each bounding box and identifies a number of individual products forthe product display location based on the number individual productbounding boxes within each bounding box. Thus, the images are capturedat a time when the product display areas are fully stocked, whichprovides the product-detection model with a template of what eachproduct display area or shelf 105 should ideally look like and againstwhich to compare images captured by the cameras 100 going forward toperform out-of-stock detection in the deployment stage.

Accordingly, in one embodiment, the out-of-stock detection systemidentifies a number of individual product bounding boxes within eachbounding box 120 identified for the product based on the product label115. FIG. 5 illustrates an example enlarged view of a portion of thetemplate image received from the camera 100 in FIG. 2 that shows aproduct display location with individual product bounding boxes 125 thatare within the bounding box 120 for the product display location. Inthis example, the product label 115 for this product display locationidentifies the product 110 as soup and there are three cans of soup.Thus, in this example, each individual can of soup has been identifiedand an individual product bounding box 125 has been generated for eachcan of soup. Accordingly, in one embodiment, the out-of-stock detectionsystem defines a product display location as full based on theidentified number of individual product bounding boxes 125 within eachbounding box 120 of the template image. Thus, the result of theonboarding process, in one embodiment, is a set of template images orimage data showing fully stocked shelf units segmented and labeled byproduct. Alternatively, the template images do not necessarily need toreflect stock or fully stocked information. Instead, the template imagecould simply identify the location of individual product displaylocations with an identification of their corresponding product bybounding box. This initial onboarding process can also be performed, forexample, when the layout or planogram of the store is changed and theout-of-stock detection system needs to reregister a new set of productswithin a particular camera's field of view.

Thus, in this embodiment, the product-detection model generatesindividual product bounding boxes 125 for each bounding box 120 andcompares the number of individual products 110 within each displaylocation from the image to the number of product associated with thefully stocked shelf unit of the template image and determines that thenumber of “Soda” products identified from the image does not match thenumber associated with the template image for shelf unit 105 b. Thus, inresponse to determining that the number of individual products withinthe display location does not match the number of product associatedwith the fully stocked product display area of the template image, theout-of-stock detection system generates an out-of-stock notification for“soda” to a store client device.

Accordingly, in the deployment stage, the out-of-stock detection systemmaintains the template image for the product display areas or shelves inthe store. Each product display area includes a number of productdisplay locations for displaying a predetermined number of products ofthe same type and/or brand. The template image of each display areacorresponds to a fully stocked product display area where the productdisplay locations are full with their corresponding product.

The out-of-stock detection system receives image data of a productdisplay area from one or more cameras periodically, as described above.The out-of-stock detection system, in this embodiment, identifiesproduct labels in the image data and generates bounding boxes. Thesystem generates a set of individual product bounding boxes that eachidentify an individual product within the display location correspondingto a single bounding box. Accordingly, the system compares the number ofindividual products within the display location from the image to thenumber of product associated with the fully stocked product display areaof the template image for each display location within the productdisplay area. In response to determining that the number of individualproducts within the display location does not match the number ofproduct associated with the fully stocked product display area of thetemplate image, the system generating an out-of-stock notification forthe product corresponding to the product display location to a storeclient device.

Thus, in the deployment stage, the cameras 100 capture an image an hour,for example, and for each image, for loops over each template, crops theregion corresponding to each bounding box and forward passes thattemplate over the captured image to predict whether the productcorresponding to each bounding box is in stock, out of stock, low (e.g.,1-3 products left for that SKU), bad template (e.g., the camera shiftedor a stocker changed the shelf unit organization dramatically),Planogram compliance issue (e.g., wrong product is there), or occlusion(e.g., the area of the shelf unit is blocked by an individual, cart,etc.). Then the out-of-stock detection system may compile a list ofaction items to send to a team of store associate stockers. In oneembodiment, this list of action items is provided via an applicationassociated with the out-of-stock detection system in the store in“Stocker Action Tools” which could be delivered to the store associatesvia text or an application notification via a smartphone or tabletcomputer (i.e., store client device 150). The list of action items caninclude out-of-stock notifications, incorrect prices on tags, product inwrong location, and so forth. Moreover, the out-of-stock detectionsystem may monitor how long it takes the store associate to respond toor take action on the action items, such as restocking an out-of-stockproduct, since the store associate, in one embodiment manually clearsthe action items from within the application once they are finishedrestocking the product, for example.

Camera Mounting Configurations

FIG. 6 shows an example top view of multiple cameras 100 and theircorresponding fields of view mounted on top of shelf units 105 a-105 din a store, in accordance with various embodiments. In this example,there are multiple cameras 100 on shelf units 105 a-105 d that eachcomprise an aisle in the store. Each camera 100 includes a motor thatallows the camera to rotate and capture an image of the shelf units oneach opposing side of the shelf unit on which a respective camera 100 ismounted. For example, a camera 100 mounted on shelf unit 105 b cancapture images of shelf unit 105 a and rotate to capture images of shelfunit 105 c. Thus, the square footage of shelf unit space that a singlecamera 100 covers is double when the cameras 100 are mounted on top ofthe shelf units 105 relative to having the cameras 100 mounted in themiddle of each shelf units 105 (e.g., ˜20 ft. vs ˜10 ft.). Moreover,another benefit of mounting the cameras 100 on top of shelf units 105 isthe reduction of glare off of refrigerator glass doors. Glare capturedin the images caused by the ceiling lighting of the store can blockimportant product identifying and void detection information in theimages. For example, if the cameras 100 are below the top of the glassof a refrigerator, then the ceiling lights reflect off of the glassdownward and may hit the cameras 100. Therefore, mounting cameras 100 ontop of the shelf units 105 or refrigerators minimizes or eliminates thepotential of glare from hitting the cameras 100 and, thereby, occludingthe product identifying information.

Also, shown in FIG. 6 are solar panels 130 for powering the cameras 100and their image data transmissions to the out-of-stock detection system.In other embodiments, the cameras 100 could be powered by a power outletor by using batteries. Store shelf units are not typically provided withpower outlets and batteries require regular changing. Thus, solar panelsthat can use light for the store lighting system may be advantageous.However, a system dependent on indoor solar lighting must make attemptsto converse energy usage where possible.

FIG. 7 shows an example top view of shelf units in a store that eachinclude a track 135 along which a camera 100 is movable mounted, inaccordance with various embodiments. In this embodiment, a camera 100captures images at different points along the track 135. As describedabove, the cameras 100 may include a motor that allows each camera torotate and capture an image of the shelf units on each opposing side ofthe shelf unit on which the track 135 is mounted. Thus, for eachposition along track 135, the camera can pan from left to rightcapturing images incrementally down track 135. Allowing for translation,the number of cameras needed per aisle is relatively low compared to theembodiment shown in FIG. 5. Track 135 may also be mounted on the bottomor middle of the shelf unit and, once an hour or other period of time,cause the camera 100 to translate a small distance down the track 135,capture an image, send it to the out-of-stock detection system, thenmove a bit further down the track 135, and so on. While this methodwould increase installation and maintenance costs relative to the methoddescribed with respect to FIG. 6, it would permit the cost effective useof a relatively higher resolution camera relative to the methoddescribed with respect to FIG. 6.

FIG. 8 shows another example of a movable mounted camera 100, inaccordance with various embodiments. In this embodiment, a camera 100 ismovable attached to a cable 140 or zip line hanging above shelf unit 105a from which camera 100 captures images at different points along thecable 140 in a manner similar to that described above with respect toFIG. 7. As described above, the cameras 100 may include two motors. Onemotor that moves camera 100 back and forth along cable 140 and a secondmotor that allows each camera 100 to rotate and capture an image of theshelf units 105 on each opposing side of the shelf unit 105 a abovewhich the cable 140 is hanging. Thus, for each position along cable 140,the camera 100 can pan from left to right capturing images incrementallydown cable 140. As above, this allows for translation, which renders thenumber of cameras needed per aisle relatively lower compared to theembodiment shown in FIG. 5. Moreover, in one embodiment, the movablecamera 100 could be mounted on a drone above shelf unit 105 a from whichcamera 100 captures images.

Planogram Compliance

Moreover, the out-of-stock detection system 160 provides a tool forstore associates to update the planogram when a shelf is changed in thestore and relay that information directly to the backend of the system.After an associate changes a shelf, the associate may use store clientdevice 150 showing the previous picture of the shelf and the previousproducts. The system, from the store client device (e.g., a tabletcomputer), enables the store associate to cause a picture to be capturedfrom the relevant shelf camera 100 and manually mark the image as thenew template image for use as a template by the system. The system thenpresents both the new and old images side by side to the storeassociate, allowing the store associate to select the products thatchanged on the shelf and input their corresponding universal productcode (UPC). Using the UPC, the system would then be able toautomatically fill out the remaining information.

In another embodiment, with enough resolution, the camera 100 would beable to read the tags on the shelf directly. The camera 100 or a remoteserver of the system can then process the image, use a tag detectionalgorithm to determine if any tags have been added or removed and thenperform OCR to determine if any of the tags have changed. If the producton the shelf has changed the system can then update the planogramautomatically. Similarly, if the system is uncertain about anyparticular tag and the camera is able to pan, tilt and/or zoom, thecamera 100 may then angle itself to provide a clearer image of thatparticular tag.

Example System Environment and Architecture

FIG. 9 illustrates a system environment for an out-of-stock detectionsystem, in accordance with various embodiments. FIG. 9 includes one ormore cameras 100, a store client device 150, a network 155, and anout-of-stock detection system 160. Alternate embodiments may includemore, fewer, or different components and the functionality of theillustrated components may be divided between the components differentlyfrom how it is described below.

An out-of-stock detection system 160 notifies store management that aproduct is out of stock. Stores, such as grocery stores, include anumber of aisles created by rows of shelves on which products arestocked and the location of the products within the store is organized,albeit often loosely, in a predetermined layout known as a planogram.Each camera 100 is associated with a unique camera ID and mounted on ashelf with a predetermined set of products within the camera's field ofview known to the out-of-stock detection system 160 based on theplanogram of the store. Each camera 100, therefore, collects image datafor a predetermined set of products on opposing shelves for on-shelfinventory tracking and out of stock detection. In one embodiment, thecamera 100 is equipped with a motor to cause the camera 100 to pan, forexample, from the top left of the opposing shelf, to the top right,angle downward, and then pan from left to right again in an iterativemanner until it hits the bottom right. This can be achieved using 1-5 MPcamera, for example, by effectively scanning the viewable area of thecamera with a 20-40 MP camera. Moreover, each camera 100 may, along withone or more images of the opposing shelf unit, send information aboutthe camera 100 to the out-of-stock detection system, such as a uniquedevice ID, battery level, external battery connection, IP address,software version number. Moreover, since store shelves are roughlyplanar and the camera 100 are often mounted on the other shelf acrossthe aisle and directed as close to normal the tracked shelf, thedistance from the top left corner of the tracked product region and thecenter tracked product region, the camera must have an appropriate depthof field to be able to focus in those areas. Each camera 100 may beequipped with a cellular connectivity modem for communicating directlywith a cell tower, for example. In another embodiment, each camera 100may be equipped with a Wi-Fi chip and connected straight to apre-existing network.

The store client device 150 (e.g., a desktop computer, mobile device,tablet computer, etc.) receives information about the status of thestore from the out-of-stock detection system 160 and presents theinformation to a store associate (e.g., a store owner, manager, oremployee). For example, the store client device 150 may present a storeassociate with information about which items are out of stock and whenthe items were first detected to be out of stock. The store clientdevice 150 may also present a map that indicates where, in the store,out-of-stock items are located. In some embodiments, the store clientdevice 150 presents images within which the out-of-stock detectionsystem 160 has detected out-of-stock items. These images may includebounding boxes that identify where in the image an out-of-stock item islocated.

The cameras 100 and the store client device 150 can communicate with theout-of-stock detection system 160 via the network 155, which maycomprise any combination of local area and wide area networks employingwired or wireless communication links. In one embodiment, the network155 uses standard communications technologies and protocols. Forexample, the network 155 includes communication links using technologiessuch as Ethernet, 802.11, worldwide interoperability for microwaveaccess (WiMAX), 3G, 4G, code division multiple access (CDMA), digitalsubscriber line (DSL), etc. Examples of networking protocols used forcommunicating via the network 155 include multiprotocol label switching(MPLS), transmission control protocol/Internet protocol (TCP/IP),hypertext transport protocol (HTTP), simple mail transfer protocol(SMTP), and file transfer protocol (FTP). Data exchanged over thenetwork 155 may be represented using any format, such as hypertextmarkup language (HTML) or extensible markup language (XML). In someembodiments, all or some of the communication links of the network 155may be encrypted. Moreover, the images can be communicated via Bluetoothor radio. For example, each individual camera may be equipped with acellular connectivity modem that communicates directly with a celltower. If the modem can transfer data then the camera can send the imageand information directly to the cloud for processing by the out-of-stockdetection system 160. The camera may also send a SMS based alert withthe current state of a shelf unit.

The out-of-stock detection system 160 detects out-of-stock items withinthe store based on images received from the cameras 100. Theout-of-stock detection system 160 may be located within the store orremotely. The out-of-stock detection system 160, illustrated in FIG. 9,includes an image collection module 165, a product detection module 170,an out-of-stock detection module 175, a user interface module 180, and adata store 185. Alternate embodiments may include more, fewer, ordifferent components from those illustrated in FIG. 9, and thefunctionality of each component may be divided between the componentsdifferently from the description below. Additionally, each component mayperform their respective functionalities in response to a request from ahuman, or automatically without human intervention.

The image collection module 165 collects images from the cameras 100and, in some embodiments, from the store client device 150. The imagecollection module 165 stores collected images and image labeling data inthe data store 190. The product detection module 150 detects products inimages captured by the cameras 100. In some embodiments, the productdetection module 150 identifies products in the received imagesautomatically. For example, the product detection module 150 may applyan optical character recognition (OCR) algorithm to the received imagesto identify text in the images, and may determine which products arecaptured in the image based on the text (e.g., based on whether the textnames a product or a brand associated with the product). The productdetection module 150 also may use a barcode detection algorithm todetect barcodes within the images and identify the products based on thebarcodes. For example, store shelves may display a product label thatcontains a barcode, or written numerical values that represent the stockkeeping unit, for each product on the shelves, and the product detectionmodule 150 may identify the product above each product label as theproduct associated with the barcode or stock keeping unit.Alternatively, the product detection module 150 detects products withinthe images by requesting that the store associate identify the productsin the images using the store client device 150.

In some embodiments, the product detection module 150 uses amachine-learned product-detection model to detect the products in theimages. The product-detection model can be trained based on templateimages that have been labeled by the store associate or other humanlabeler. For example, the training data may label a product asout-of-stock only when a shelf is completely empty. In this instancethen, the product-detection model will tend to generate out-of-stocknotifications when the shelf is completely empty. In another instance,if the training data may include labeling shelves that are out-of-stockwhen the shelves are mostly empty (e.g., have only one or two productson the shelf), then the model will tend to identify partly stockedproducts (or almost empty shelves) as out-of-stock. In some embodiments,multiple models are trained where one model detects empty shelves andanother model detects partly empty shelves. In some embodiments, theproduct-detection model is trained based on labeled images of theproducts offered for sale by the store. The product-detection modelidentifies the products in the images aided by what images should be inthe images based on the planogram and camera ID associated with eachimage. In some embodiments, the product-detection model generatesbounding boxes for each product and determines a likelihood that theproduct-detection model's prediction is correct. The product-detectionmodel can be a convolutional neural network that has been trained usinglabeled training data via Stochastic Gradient Descent based on thetemplates images. Additionally, the product detection module 150 usesthe product-detection model to compare images received from the one ormore cameras 100 to template images captured by the store client device150 or to higher resolution images captured by the cameras 100 in theonboarding process.

In some embodiments, the product detection module 150 detects emptyportions of shelves or display areas within the store. The productdetection module 150 can generate bounding boxes that identify portionsof images received from the cameras 100 where a product is out of stock.While the bounding boxes may identify portions of images where a productis out of stock, the bounding boxes may not actually identify whichproduct is out of stock. The bounding boxes may be generated using amachine-learned model that is trained based on template images of emptyshelves or display areas within the store. The machine-learned model mayalso be trained based on template images with one or more stockedproducts as well.

The out-of-stock detection module 175 detects out-of-stock items inimages received from the camera 100. The out-of-stock detection module175 uses products detected by the product detection module 150 to detectout-of-stock items in the received images. For example, the out-of-stockdetection module 175 may use the template images to determine whichproducts are supposed to be detected in an image receive from aparticular camera 100 based on the unique camera ID. Moreover, theout-of-stock detection module 175 may determine which products aresupposed to be detected in the image by identifying one or more templateimages that capture areas of the store that are captured by the receivedimages. If the out-of-stock detection module 175 determines that an itemis not detected in the image received from the camera 100 that issupposed to be detected in the image, the out-of-stock detection module175 determines that the item is out of stock.

In some embodiments, the out-of-stock detection module 175 uses productlabels on shelves to determine if an item is out of stock. Theout-of-stock detection module 175 can detect product labels boundingboxes in the images received from the cameras 100 and can extractinformation from each product label like the barcode, the stock keepingunit, the price, the sale price if it is on sale, the name of theproduct, and any other visual information that is on the product label.For example, the out-of-stock detection module 175 may extract the nameof a product, stock keeping unit, or a barcode from the product labeland the out-of-stock detection module 175 may determine that theproducts identified by the product labels should be detected in theimage received from the camera 100 near that product label. If theout-of-stock detection module 175 does not detect the items identifiedby the product label near (e.g., immediately above or below) that tag,the out-of-stock detection module 175 determines that the items are outof stock.

In some embodiments, the out-of-stock detection module 175 comparesbounding boxes identifying empty shelves or display areas to informationfrom identified product labels to identify out-of-stock items. Theout-of-stock detection module 175 may use a machine-learned model toidentify the locations of product labels within images received from thecamera 100. The out-of-stock detection module 175 may then use anoptical character recognition (OCR) algorithm to extract informationfrom a product label that describes the product. For example, theout-of-stock detection module 175 may extract the name of the product, aproduct identifier (e.g., the stock keeping unit or the UniversalProduct Code for the product), or the price of the product from theproduct label. The out-of-stock detection module 175 can then associateproduct labels with empty portions of the store by comparing thelocation of the bounding boxes identifying empty shelves or displayareas to the locations of identified product labels to identify whichproduct labels correspond to the bounding boxes. In some embodiments,the out-of-stock detection module 175 associates each bounding boxidentifying empty shelves or display areas with the closest identifiedproduct label. After associating the bounding boxes with the productlabels, the out-of-stock detection system can identify the products thatare out of stock within each bounding box based on the informationextracted from the product labels.

When the out-of-stock detection module 175 determines that an item isout of stock, the out-of-stock detection module 175 notifies the storeclient device 150 that the item is out of stock. The out-of-stockdetection module 175 may transmit an item identifier for theout-of-stock item as well as a timestamp of when the item was detectedas being out of stock. In some embodiments, the out-of-stock detectionmodule 175 generates a bounding box that describes where theout-of-stock item would be in the image received from the camera 100.The out-of-stock detection module 175 transmits the image with thebounding box to the store client device 150 for presentation to thestore associate.

The user interface module 180 interfaces with the store client device150. The interface generation module 170 may receive and route messagesbetween the out-of-stock detection system 160, the cameras 100, and thestore client device 150, for example, instant messages, queued messages(e.g., email), text messages, or short message service (SMS) messages.The user interface module 180 may provide application programminginterface (API) functionality to send data directly to native clientdevice operating systems, such as IOS®, ANDROID™, WEBOS®, or RIM®.

The user interface module 180 generates user interfaces, such as webpages, for the out-of-stock system 130. The user interfaces aredisplayed to the store associate through the store client device 150.The user interface module 180 configures a user interface based on thedevice used to present it. For example, a user interface for asmartphone with a touchscreen may be configured differently from a userinterface for a web browser on a computer.

The user interface module 180 can provide a user interface to the storeclient device 150 for capturing template images of store shelves thathold products for sale by the store. Additionally, the user interfacemodule 180 may provide a user interface to the store client device 150for labeling products in template images. The user interface module 180receives images from the camera 100 and the store client device 150 andstores the images in the data store 185.

The data store 185 stores data used by the out-of-stock detection system160. For example, the data store 185 can store images from the camera100 and the store client device 150. The data store 185 can also storelocation information associated with template images, and can storeproducts identified in images by the product detection module 150. Thedata store 185 can also store product information, a store map orplanogram, shopper information, or shopper location information. In someembodiments, the data store 185 also stores product-detection models orother machine-learned models generated by the out-of-stock detectionsystem 160. In one embodiment, at least some of the computation occurslocally on the camera system itself that includes a relatively smalldeep learning neural network. Then a larger amount of the computation(big deep learning models) would happen on a server that is deployedinside the store. Accordingly, in one embodiment, an additional portionof the computation could happen in the cloud via a back end of theout-of-stock detection system 160.

Additional Considerations

The foregoing description of the embodiments has been presented for thepurpose of illustration; it is not intended to be exhaustive or to limitthe patent rights to the precise forms disclosed. Persons skilled in therelevant art can appreciate that many modifications and variations arepossible in light of the above disclosure.

Some portions of this description describe the embodiments in terms ofalgorithms and symbolic representations of operations on information.These algorithmic descriptions and representations are commonly used bythose skilled in the data processing arts to convey the substance oftheir work effectively to others skilled in the art. These operations,while described functionally, computationally, or logically, areunderstood to be implemented by computer programs or equivalentelectrical circuits, microcode, or the like. Furthermore, it has alsoproven convenient at times, to refer to these arrangements of operationsas modules, without loss of generality. The described operations andtheir associated modules may be embodied in software, firmware,hardware, or any combinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In some embodiments, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments may also relate to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, and/or it may comprise a general-purpose computingdevice selectively activated or reconfigured by a computer programstored in the computer. Such a computer program may be stored in anon-transitory, tangible computer readable storage medium, or any typeof media suitable for storing electronic instructions, which may becoupled to a computer system bus. Furthermore, any computing systemsreferred to in the specification may include a single processor or maybe architectures employing multiple processor designs for increasedcomputing capability.

Embodiments may also relate to a product that is produced by a computingprocess described herein. Such a product may comprise informationresulting from a computing process, where the information is stored on anon-transitory, tangible computer readable storage medium and mayinclude any embodiment of a computer program product or other datacombination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the patent rights be limitednot by this detailed description, but rather by any claims that issue onan application based hereon. Accordingly, the disclosure of theembodiments is intended to be illustrative, but not limiting, of thescope of the patent rights, which is set forth in the following claims.

What is claimed is:
 1. A method for performing a stock detection processcomprising: capturing image data of a product display area, the imagedata including a bounding box that encompasses a plurality of a givenproduct; providing the bounding box as input to a classifier model,receiving, as output from the classifier model, an indication that thegiven product is in a restock state, wherein the classifier model is amachine learning model that is trained to output that the given productis in a restock state where the bounding box features up to a thresholdamount of the given product, the bounding box including two or more ofthe given product; and responsive to receiving the output, sending anotification that the given product is determined to be in a restockstate to a client device.
 2. The method of claim 1, wherein the productdisplay area comprises a different product from the given product, andwherein the image data includes a different bounding box thatencompasses the different product.
 3. The method of claim 1, furthercomprising determining that the bounding box encompasses the pluralityof the given product by: accessing a data entry that maps a position ofthe product display area to the given product; and determining that thebounding box corresponds to the mapped position.
 4. The method of claim3, wherein the position is mapped to the product display area based onplacement of a product label within the position, the product labeldescribing the given product.
 5. The method of claim 4, wherein thebounding box is generated based on the placement of the product label.6. The method of claim 1, further comprising determining that thebounding box encompasses the plurality of the given product by:accessing at least a portion of a planogram corresponding to a shelfunit including the product display area; and determining a position ofthe given product by overlaying at least the portion of the planogramonto an image of the shelf unit.
 7. The method of claim 1, wherein theproduct display area corresponds to a shelf unit, and wherein a camerathat captures the image data is mounted on a movable track in view ofthe plurality of shelf units comprising within an aisle comprising aplurality of shelf units, the camera capturing an image of each productdisplay area along the aisle and sending captured images with respectiveposition data for the camera along the movable track, at least one ofthe captured images including the image data.
 8. A non-transitorycomputer-readable medium comprising memory with instructions encodedthereon for performing a stock detection process, the instructions whenexecuted causing at least one processor to perform operations, theinstructions comprising instructions to: capture image data of a productdisplay area, the image data including a bounding box that encompasses aplurality of a given product; provide the bounding box as input to aclassifier model, receive, as output from the classifier model, anindication that the given product is in a restock state, wherein theclassifier model is a machine learning model that is trained to outputthat the given product is in a restock state where the bounding boxfeatures up to a threshold amount of the given product, the bounding boxincluding two or more of the given product; and responsive to receivingthe output, send a notification that the given product is determined tobe in a restock state to a client device.
 9. The non-transitorycomputer-readable medium of claim 8, wherein the product display areacomprises a different product from the given product, and wherein theimage data includes a different bounding box that encompasses thedifferent product.
 10. The non-transitory computer-readable medium ofclaim 8, wherein the instructions further comprise instructions todetermine that the bounding box encompasses the plurality of the givenproduct by: accessing a data entry that maps a position of the productdisplay area to the given product; and determining that the bounding boxcorresponds to the mapped position.
 11. The non-transitorycomputer-readable medium of claim 10, wherein the position is mapped tothe product display area based on placement of a product label withinthe position, the product label describing the given product.
 12. Thenon-transitory computer-readable medium of claim 11, wherein thebounding box is generated based on the placement of the product label.13. The non-transitory computer-readable medium of claim 8, wherein theinstructions further comprise instructions to determine that thebounding box encompasses the plurality of the given product by:accessing at least a portion of a planogram corresponding to a shelfunit including the product display area; and determining a position ofthe given product by overlaying at least the portion of the planogramonto an image of the shelf unit.
 14. The non-transitorycomputer-readable medium of claim 8, wherein the product display areacorresponds to a shelf unit, and wherein a camera that captures theimage data is mounted on a movable track in view of the plurality ofshelf units comprising within an aisle comprising a plurality of shelfunits, the camera capturing an image of each product display area alongthe aisle and sending captured images with respective position data forthe camera along the movable track, at least one of the captured imagesincluding the image data.
 15. A system comprising: memory withinstructions encoded thereon for performing a stock detection process;and one or more processors that, when executing the instructions, arecaused to perform operations comprising: capturing image data of aproduct display area, the image data including a bounding box thatencompasses a plurality of a given product; providing the bounding boxas input to a classifier model, receiving, as output from the classifiermodel, an indication that the given product is in a restock state,wherein the classifier model is a machine learning model that is trainedto output that the given product is in a restock state where thebounding box features up to a threshold amount of the given product, thebounding box including two or more of the given product; and responsiveto receiving the output, sending a notification that the given productis determined to be in a restock state to a client device.
 16. Thesystem of claim 15, wherein the product display area comprises adifferent product from the given product, and wherein the image dataincludes a different bounding box that encompasses the differentproduct.
 17. The system of claim 15, the operations further comprisingdetermining that the bounding box encompasses the plurality of the givenproduct by: accessing a data entry that maps a position of the productdisplay area to the given product; and determining that the bounding boxcorresponds to the mapped position.
 18. The system of claim 17, whereinthe position is mapped to the product display area based on placement ofa product label within the position, the product label describing thegiven product.
 19. The system of claim 18, wherein the bounding box isgenerated based on the placement of the product label.
 20. The system ofclaim 15, the operations further comprising determining that thebounding box encompasses the plurality of the given product by:accessing at least a portion of a planogram corresponding to a shelfunit including the product display area; and determining a position ofthe given product by overlaying at least the portion of the planogramonto an image of the shelf unit.